Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import time
+import sys
+import traceback
+
+# Global variables to store error information
+error_message = ""
+
+# Global variables for model and tokenizer
+model = None
+tokenizer = None
+device = None
+
+# Load the model and tokenizer from Hugging Face
+model_name = "ambrosfitz/history-qa-flan-t5-large"
+try:
+    global model, tokenizer, device
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+except Exception as e:
+    error_message = f"Error loading model or tokenizer: {str(e)}\n{traceback.format_exc()}"
+print(error_message)
+
+def generate_qa(text, max_length=512):
+    global model, tokenizer, device
+    try:
+        input_text = f"Generate a history question and answer based on this text: {text}"
+        input_ids = tokenizer(input_text, return_tensors="pt", max_length=max_length, truncation=True).input_ids.to(device)
+        
+        with torch.no_grad():
+            outputs = model.generate(input_ids, max_length=max_length, num_return_sequences=1, do_sample=True, temperature=0.7)
+            generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Parse the generated text
+        parts = generated_text.split("Question: ")
+        if len(parts) > 1:
+            qa_parts = parts[1].split("Answer: ")
+            question = qa_parts[0].strip()
+            answer = qa_parts[1].strip() if len(qa_parts) > 1 else "No answer provided."
+            return f"Question: {question}\n\nAnswer: {answer}"
+        else:
+            return "Unable to generate a proper question and answer. Please try again with a different input."
+    except Exception as e:
+        return f"An error occurred: {str(e)}\n{traceback.format_exc()}"
+
+def slow_qa(message, history):
+    try:
+        full_response = generate_qa(message)
+        for i in range(len(full_response)):
+            time.sleep(0.01)
+            yield full_response[:i+1]
+    except Exception as e:
+        yield f"An error occurred: {str(e)}\n{traceback.format_exc()}"
+
+# Create and launch the Gradio interface
+try:
+    iface = gr.ChatInterface(
+        slow_qa,
+        chatbot=gr.Chatbot(height=500),
+        textbox=gr.Textbox(placeholder="Enter historical text here...", container=False, scale=7),
+        title="History Q&A Generator (FLAN-T5)",
+        description="Enter a piece of historical text, and the model will generate a related question and answer.",
+        theme="soft",
+        examples=[
+            "The American Revolution was a colonial revolt that took place between 1765 and 1783.",
+            "World War II was a global conflict that lasted from 1939 to 1945, involving many of the world's nations.",
+            "The Renaissance was a period of cultural, artistic, political, and economic revival following the Middle Ages."
+        ],
+        cache_examples=False,
+        retry_btn="Regenerate",
+        undo_btn="Remove last",
+        clear_btn="Clear",
+    )
+    
+    if error_message:
+        print("Launching interface with error message.")
+    else:
+        print("Launching interface normally.")
+    iface.launch(debug=True)
+except Exception as e:
+    print(f"An error occurred while creating or launching the interface: {str(e)}\n{traceback.format_exc()}")